Companies and other organizations seek to market their products and services to consumers as effectively as they can. For example, marketers want to remain connected with their customers via email and do not want their customers to unsubscribe to their email distribution lists. In order to reduce an ‘unsubscribe rate,’ marketers and brands typically need to perform resource intensive tasks to address multiple factors before sending an email to a customer. One such factor is determining an optimum frequency of promotional emails. If too many promotional emails are sent within a given period, customers may unsubscribe from emails associated with a brand or product. Another factor is personalization. Customers may unsubscribe from an email list in response to receiving impersonal, generic emails. As a result, marketers currently spend many resources to personalize emails for a recipient (e.g., personalizing the subject or content of an email message for a particular recipient).
Current marketing programs can fall victim to the issue of high un-subscription rates. One issue with traditional marketing programs is that marketing becomes difficult when existing customers of a product or brand start unsubscribing or opting out of receiving email associated with the product or brand. This is because attracting new customers can cost a company much more (e.g., five times more) than keeping an existing customer. Also, the probability of selling to an existing customer is much higher than the probability of selling to a new prospect or lead. Thus, marketers and entities associated with products and brands (e.g., manufacturers and suppliers) do not want an email campaign to result in their existing customers un-subscribing from their email lists. Hence, reducing un-subscriptions rates is very important to the marketers and entities.
Existing techniques seek to predict a response of a potential customer (e.g., a prospective customer) to receiving an electronic message from a marketer. These techniques can use a prediction un-subscription model to determine if a prospect or lead is prone to disengage from an electronic marketing campaign based on receiving an electronic message from the marketer. The techniques attempt to predict whether a potential customer will disengage from an electronic marketing campaign by identifying features and interactions associated with the potential customer and determining whether the features and interactions indicate that the potential customer is prone to disengage from receiving further electronic messages from the marketer. In one such technique, the prediction un-subscription model may indicate a likelihood that the prospect will disengage from an electronic marketing campaign responsive to receiving an electronic marketing communication, and the model can indicate a timing when the electronic communication is unlikely lead to disengagement by the prospective customer. These existing techniques do not address un-subscription rates for current customers.
Marketers and salespeople do not want to send marketing communications that may cause an existing customer to unsubscribe (e.g., opt out of receiving future communications from the marketer). Thus, there is a need for systems that enable marketers to reduce un-subscription rates among customers. Existing techniques for reducing un-subscription rates for marketing communications are limited because these techniques do not take into account intelligence related to real time information about the context, sentiment and interaction that a customer has in relation to the marketer's product and services.